| name | bio-flow-cytometry-compensation-transformation |
| description | Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing. |
| tool_type | r |
| primary_tool | flowCore |
Version Compatibility
Reference examples tested with: flowCore 2.14+, flowStats 4.14+, flowWorkspace 4.14+, CATALYST 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>') then ?function_name to verify parameters
Notes that bite: estimateLogicle() lives in flowWorkspace (not flowCore). flowCore::spillover() on a flowFrame returns a LIST of keyword matrices (index [[1]]); flowStats::spillover() on single-stain controls returns the matrix DIRECTLY (not a list) - do not index it with $.
If code throws an error, introspect the installed package and adapt rather than retrying.
Compensation and Transformation
"Compensate and transform my cytometry data" -> Remove spillover (matrix subtraction, conventional) or unmix the full spectrum (least squares, spectral), then apply a transform so populations separate.
- R (conventional):
flowCore::compensate() then flowWorkspace::estimateLogicle() + flowCore::transform()
- R (CyTOF/mass):
CATALYST::prepData(..., transform=TRUE, cofactor=5) (arcsinh)
- R (spectral): linear UNMIXING, not compensation - see the taxonomy
The Single Most Important Modern Insight -- Compensation Corrects the Mean; It Cannot Remove Spreading Error
Conventional compensation inverts a square spillover matrix (peak-channel subtraction); spectral cytometry solves an OVERDETERMINED least-squares unmix over all detectors, with autofluorescence modeled as an extra "fluorophore." Both correct the population MEAN. Neither removes spreading error - the widening of a negative population in a spillover detector that arises from the Poisson counting statistics of the spilled-in photons (Roederer 2001 Cytometry 45:194; Nguyen 2013 Cytometry A 83:306). Compensation does not INTRODUCE spreading; it makes the pre-existing variance visible by re-centering means. The corollaries are load-bearing: (1) a smeared negative cannot be fixed by tuning the matrix - over-compensating to flatten it is data falsification; (2) spreading is fixed at PANEL DESIGN (the Spillover Spreading Matrix identifies which detector pairs to avoid for co-expressed/dim markers), never downstream; (3) calling spectral unmixing "compensation" is a category error - it is a different, overdetermined model.
Method Taxonomy
| Method | What it does | When to use | Fails when |
|---|
Acquisition-recorded $SPILLOVER | applies the cytometer-computed matrix | trustworthy single-stain setup at acquisition | controls were wrong/missing |
Computed compensation (flowStats::spillover) | estimates spillover from single-stain controls (medians) | conventional flow, controls available | poor/dim/contaminated controls |
| AutoSpill (Roca 2021 Nat Commun 12:2890) | robust-regression matrix + iterative refinement; AF as endogenous dye | high-parameter panels; messy controls | reference implementation/setup unavailable |
| Spectral unmixing (OLS/WLS/Poisson) | least-squares unmix full spectrum vs reference spectra + AF | spectral cytometers (Aurora, ID7000) | wrong/heterogeneous AF; collinear spectra |
| Logicle / biexponential | display + analysis transform, handles negatives | fluorescence flow | wrong w clips the negative population |
| arcsinh | variance-stabilizing transform | CyTOF/mass; computational pipelines | wrong cofactor compresses dim markers |
| log10 | legacy | rarely; strictly positive data | any negative values after compensation |
Decision Tree by Scenario
| Scenario | Recommended | Why |
|---|
Conventional flow, $SPILLOVER present | apply recorded matrix -> estimateLogicle | trust acquisition controls; logicle handles negatives |
| Conventional flow, no matrix | compute via flowStats::spillover from single-stains (or AutoSpill) | controls drive the matrix; AutoSpill for >12 colors |
| Spectral cytometer | UNMIX (do NOT compensate), then arcsinh at ~150/per-channel (NOT 5) | overdetermined system; spectral data is fluorescence-scale, not ion counts |
| CyTOF / mass | arcsinh cofactor 5; spillover via CATALYST compCytof if needed | metals barely spill (~1-4%), but oxide/impurity is real |
| Dim marker driving a borderline call | test per-channel cofactor (flowVS) | a fixed cofactor can manufacture/erase the population |
Compensate-Then-Transform Ordering (load-bearing)
Compensation/unmixing is LINEAR and must run on untransformed data; applying it after a nonlinear transform is mathematically invalid. estimateLogicle() must run on ALREADY-COMPENSATED data so the w/a parameters reflect the post-compensation negative spread. Negative values after compensation are expected and meaningful - do NOT clip to zero before transforming (handling negatives is the entire reason logicle/arcsinh exist; log cannot).
Apply or Compute Compensation
Goal: Apply the recorded matrix, or estimate one from single-stain controls.
Approach: compensate() takes a compensation object built from the matrix; flowStats::spillover() estimates from single-stain controls and returns the matrix directly.
library(flowCore)
comp <- compensation(spillover(fcs)[[1]])
fcs_comp <- compensate(fcs, comp)
library(flowStats)
ctrls <- read.flowSet(list.files('controls', pattern = '\\.fcs$', full.names = TRUE))
comp_matrix <- spillover(ctrls, unstained = 'Unstained.fcs', fsc = 'FSC-A', ssc = 'SSC-A',
patt = '-A$', method = 'median')
Logicle / Biexponential Transform (fluorescence)
Goal: Display and analyze compensated fluorescence with negatives handled honestly.
Approach: estimateLogicle() (flowWorkspace) derives w from the data's most-negative events; apply with transform().
library(flowWorkspace)
fluo <- colnames(fcs_comp)[grepl('-A$', colnames(fcs_comp)) & !grepl('FSC|SSC', colnames(fcs_comp))]
lgcl <- estimateLogicle(fcs_comp, channels = fluo)
fcs_t <- transform(fcs_comp, lgcl)
Arcsinh Transform (CyTOF cofactor 5; fluorescence/spectral ~150 or per-channel)
Goal: Variance-stabilize mass-cytometry counts (or any pipeline feeding clustering).
Approach: asinh(x/cofactor); flowCore's arcsinhTransform is asinh(a + b*x) + c, so set b=1/cofactor. CATALYST prepData defaults cofactor=5.
COFACTOR <- 5
asinhT <- arcsinhTransform(transformationId = 'asinh', a = 0, b = 1/COFACTOR, c = 0)
fcs_t <- transform(fcs, transformList(marker_channels, asinhT))
sce <- CATALYST::prepData(fs, panel, md, transform = TRUE, cofactor = COFACTOR)
Per-Method Failure Modes
Over-compensation (negative pull-down)
Trigger: matrix slope over-estimated from dim controls. Mechanism: subtraction overshoots. Symptom: negative population pulled below zero, "comma" shape. Fix: controls at least as bright as the sample; AutoSpill regression; never hand-tune to flatten spread.
Wrong logicle width clips negatives
Trigger: fixed w instead of estimateLogicle. Mechanism: linear region too narrow. Symptom: negative population piled on the axis. Fix: estimate w on compensated data.
Cofactor compresses a dim marker
Trigger: cofactor 5 on fluorescence (or 150 on CyTOF). Mechanism: linear region mismatched to the noise band. Symptom: dim-positive collapses into the negative; clusters don't reproduce. Fix: 5 for CyTOF, ~150 for fluorescence; per-channel via flowVS::estParamFlowVS.
Compensating spectral data
Trigger: treating Aurora data as conventional. Mechanism: subtraction is the wrong model for an overdetermined system. Symptom: residual spread, false positives. Fix: unmix against single-stain reference spectra + unstained AF.
Quantitative Thresholds
| Threshold | Source | Rationale |
|---|
| arcsinh cofactor = 5 (mass) | Nowicka 2017 F1000Res 6:748 (CATALYST workflow) | matches CyTOF ion-count near-zero noise band |
| arcsinh cofactor ~150 (fluorescence) | community/CATALYST convention (not a derived optimum) | PMT photon scale is far larger; per-channel flowVS supersedes |
| comp control >= sample brightness | Roederer 2001 Cytometry 45:194 | slope estimated over the widest lever arm; extrapolation amplifies error |
| spreading is intensity-dependent (~sqrt of signal) | Nguyen 2013 Cytometry A 83:306 | SSM is normalized to be gain-independent for panel design |
Common Errors
| Error / symptom | Cause | Solution |
|---|
compensate() channel mismatch | matrix colnames != FCS channels | align names before compensate |
| all-negative after transform | transform applied before/without compensation | compensate on linear data first |
estimateLogicle not found | called from flowCore | it lives in flowWorkspace |
arcsinhTransform ignores "cofactor" | param is b, not cofactor | set b = 1/cofactor |
References
- Roederer 2001 Cytometry 45(3):194-205 — spreading error / compensation artifacts.
- Nguyen 2013 Cytometry A 83(3):306-315 — spillover spreading matrix; panel design.
- Roca 2021 Nat Commun 12:2890 — AutoSpill robust-regression compensation.
- Parks 2006 Cytometry A 69(6):541-551 — logicle display.
- Moore & Parks 2012 Cytometry A 81(4):273-277 — logicle operational update.
- Bendall 2011 Science 332(6030):687-696 — CyTOF mass cytometry; arcsinh-median analysis.
- Nowicka 2017 F1000Research 6:748 — CATALYST workflow; codifies the cofactor-5 convention.
- Azad 2016 BMC Bioinformatics 17:291 — flowVS per-channel cofactor.
- Chevrier 2018 Cell Syst 6(5):612-620 — CyTOF spillover compensation (CATALYST).
Related Skills
- fcs-handling - Load FCS and retrieve the spillover keyword first
- gating-analysis - Gate on the transformed, compensated scale
- clustering-phenotyping - Cluster compensated, transformed data
- cytometry-qc - QC before and after preprocessing
- imaging-mass-cytometry/data-preprocessing - Shared arcsinh/metal-channel conventions
- spatial-transcriptomics/spatial-proteomics - Spectral/metal unmixing context
